کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4464621 1313837 2016 8 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Mapping cultivable land from satellite imagery with clustering algorithms
ترجمه فارسی عنوان
نقشه برداری زمین های قابل کشت از تصاویر ماهواره ای با الگوریتم های خوشه بندی
کلمات کلیدی
تعیین حدود زمین؛ کشاورزی؛ خوشه بندی؛ زمین های قابل کشت. ماهواره؛ خدمات هوشمند کشاورزی
Land delimitation; Agriculture; Clustering; Cultivable land; Satellite; Smart agro-services
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
چکیده انگلیسی


• The method groups the pixels of multispectral images in two main clusters.
• All the steps involved in the method can be automated using scripts.
• A map of cultivable/non-cultivable land can be produce as outcome of the method.

Open data satellite imagery provides valuable data for the planning and decision-making processes related with environmental domains. Specifically, agriculture uses remote sensing in a wide range of services, ranging from monitoring the health of the crops to forecasting the spread of crop diseases. In particular, this paper focuses on a methodology for the automatic delimitation of cultivable land by means of machine learning algorithms and satellite data. The method uses a partition clustering algorithm called Partitioning Around Medoids and considers the quality of the clusters obtained for each satellite band in order to evaluate which one better identifies cultivable land. The proposed method was tested with vineyards using as input the spectral and thermal bands of the Landsat 8 satellite. The experimental results show the great potential of this method for cultivable land monitoring from remote-sensed multispectral imagery.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 49, July 2016, Pages 99–106
نویسندگان
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